https://github.com/instillai/machine-learning-course
:speech_balloon: Machine Learning Course with Python:
https://github.com/instillai/machine-learning-course
algorithms artificial-intelligence machine-learning machine-learning-algorithms python
Last synced: about 1 month ago
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:speech_balloon: Machine Learning Course with Python:
- Host: GitHub
- URL: https://github.com/instillai/machine-learning-course
- Owner: instillai
- Created: 2019-02-15T00:23:19.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2024-11-27T03:47:57.000Z (7 months ago)
- Last Synced: 2025-04-11T22:18:38.539Z (2 months ago)
- Topics: algorithms, artificial-intelligence, machine-learning, machine-learning-algorithms, python
- Language: Python
- Homepage: https://machine-learning-course.readthedocs.io/en/latest/
- Size: 13.5 MB
- Stars: 7,040
- Watchers: 304
- Forks: 1,256
- Open Issues: 1
-
Metadata Files:
- Readme: README.rst
Awesome Lists containing this project
- awesome-repositories - instillai/machine-learning-course - :speech_balloon: Machine Learning Course with Python: (Python)
- StarryDivineSky - instillai/machine-learning-course - learn、TensorFlow或PyTorch。课程目标是使学习者能够独立完成一些简单的机器学习项目。项目可能注重理论与实践相结合，帮助学习者理解机器学习算法的原理和应用。项目可能包含一些案例研究，展示机器学习在实际问题中的应用。学习者可以通过参与项目，提高解决实际问题的能力。项目可能提供在线讨论区或论坛，方便学习者交流和讨论。 (A01_机器学习教程)
README
###################################################
A Machine Learning Course with Python
###################################################
.. image:: https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=flat
:target: https://github.com/pyairesearch/machine-learning-for-everybody/pulls
.. image:: https://badges.frapsoft.com/os/v2/open-source.png?v=103
:target: https://github.com/ellerbrock/open-source-badge/
.. image:: https://img.shields.io/badge/Made%20with-Python-1f425f.svg
:target: https://www.python.org/
.. image:: https://img.shields.io/github/contributors/machinelearningmindset/machine-learning-course.svg
:target: https://github.com/machinelearningmindset/machine-learning-course/graphs/contributors
.. image:: https://img.shields.io/badge/book-pdf-blue.svg
:target: https://machinelearningmindset.com/wp-content/uploads/2019/06/machine-learning-course.pdf
.. image:: https://img.shields.io/badge/official-documentation-green.svg
:target: https://machine-learning-course.readthedocs.io/en/latest/
.. image:: https://img.shields.io/twitter/follow/machinemindset.svg?label=Follow&style=social
:target: https://twitter.com/machinemindset
##################
Table of Contents
##################
.. contents::
:local:
:depth: 4
================================================
Download Free Deep Learning Resource Guide
================================================
.. raw:: html
.. raw:: html
.. raw:: html
================================================
Slack Group
================================================
.. raw:: html
.. raw:: html
.. raw:: html
========================
Introduction
========================
The purpose of this project is to provide a comprehensive and yet simple course in Machine Learning using Python.
.. You can access to the full documentation with the following links: |Book| |Documentation|
.. .. |Book| image:: https://img.shields.io/badge/book-pdf-blue.svg
:target: https://machinelearningmindset.com/wp-content/uploads/2019/06/machine-learning-course.pdf
.. .. |Documentation| image:: https://img.shields.io/badge/official-documentation-green.svg
:target: https://machine-learning-course.readthedocs.io/en/latest/
============
Motivation
============
``Machine Learning``, as a tool for ``Artificial Intelligence``, is one of the most widely adopted
scientific fields. A considerable amount of literature has been published on Machine Learning.
The purpose of this project is to provide the most important aspects of ``Machine Learning`` by presenting a
series of simple and yet comprehensive tutorials using ``Python``. In this project, we built our
tutorials using many different well-known Machine Learning frameworks such as ``Scikit-learn``. In this project you will learn:
* What is the definition of Machine Learning?
* When it started and what is the trending evolution?
* What are the Machine Learning categories and subcategories?
* What are the mostly used Machine Learning algorithms and how to implement them?
=====================
Machine Learning
=====================
+--------------------------------------------------------------------+-------------------------------+
| Title | Document |
+====================================================================+===============================+
| An Introduction to Machine Learning | `Overview `_ |
+--------------------------------------------------------------------+-------------------------------+
.. _Intro: docs/source/intro/intro.rst
------------------------------------------------------------
Machine Learning Basics
------------------------------------------------------------
.. figure:: _img/intro.png
.. _lrtutorial: docs/source/content/overview/linear-regression.rst
.. _lrcode: https://github.com/machinelearningmindset/machine-learning-course/blob/master/code/overview/linear_regression/linearRegressionOneVariable.ipynb
.. _overtutorial: docs/source/content/overview/overfitting.rst
.. _overcode: code/overview/overfitting
.. _regtutorial: docs/source/content/overview/regularization.rst
.. _regcode: code/overview/regularization
.. _crosstutorial: docs/source/content/overview/crossvalidation.rst
.. _crosscode: code/overview/cross-validation
+--------------------------------------------------------------------+-------------------------------+--------------------------------+
| Title | Code | Document |
+====================================================================+===============================+================================+
| Linear Regression | `Python `_ | `Tutorial `_ |
+--------------------------------------------------------------------+-------------------------------+--------------------------------+
| Overfitting / Underfitting | `Python `_ | `Tutorial `_ |
+--------------------------------------------------------------------+-------------------------------+--------------------------------+
| Regularization | `Python `_ | `Tutorial `_ |
+--------------------------------------------------------------------+-------------------------------+--------------------------------+
| Cross-Validation | `Python `_ | `Tutorial `_ |
+--------------------------------------------------------------------+-------------------------------+--------------------------------+
------------------------------------------------------------
Supervised learning
------------------------------------------------------------
.. figure:: _img/supervised.gif
.. _dtdoc: docs/source/content/supervised/decisiontrees.rst
.. _dtcode: code/supervised/DecisionTree/decisiontrees.py
.. _knndoc: docs/source/content/supervised/knn.rst
.. _knncode: code/supervised/KNN/knn.py
.. _nbdoc: docs/source/content/supervised/bayes.rst
.. _nbcode: code/supervised/Naive_Bayes
.. _logisticrdoc: docs/source/content/supervised/logistic_regression.rst
.. _logisticrcode: supervised/Logistic_Regression/logistic_ex1.py
.. _linearsvmdoc: docs/source/content/supervised/linear_SVM.rst
.. _linearsvmcode: code/supervised/Linear_SVM/linear_svm.py
+--------------------------------------------------------------------+-------------------------------+------------------------------+
| Title | Code | Document |
+====================================================================+===============================+==============================+
| Decision Trees | `Python `_ | `Tutorial `_ |
+--------------------------------------------------------------------+-------------------------------+------------------------------+
| K-Nearest Neighbors | `Python `_ | `Tutorial `_ |
+--------------------------------------------------------------------+-------------------------------+------------------------------+
| Naive Bayes | `Python `_ | `Tutorial `_ |
+--------------------------------------------------------------------+-------------------------------+------------------------------+
| Logistic Regression | `Python `_ | `Tutorial `_ |
+--------------------------------------------------------------------+-------------------------------+------------------------------+
| Support Vector Machines | `Python `_ | `Tutorial `_ |
+--------------------------------------------------------------------+-------------------------------+------------------------------+
------------------------------------------------------------
Unsupervised learning
------------------------------------------------------------
.. figure:: _img/unsupervised.gif
.. _clusteringdoc: docs/source/content/unsupervised/clustering.rst
.. _clusteringcode: code/unsupervised/Clustering
.. _pcadoc: docs/source/content/unsupervised/pca.rst
.. _pcacode: code/unsupervised/PCA
+--------------------------------------------------------------------+-------------------------------+--------------------------------+
| Title | Code | Document |
+====================================================================+===============================+================================+
| Clustering | `Python `_ | `Tutorial `_ |
+--------------------------------------------------------------------+-------------------------------+--------------------------------+
| Principal Components Analysis | `Python `_ | `Tutorial `_ |
+--------------------------------------------------------------------+-------------------------------+--------------------------------+
------------------------------------------------------------
Deep Learning
------------------------------------------------------------
.. figure:: _img/deeplearning.png
.. _mlpdoc: docs/source/content/deep_learning/mlp.rst
.. _mlpcode: code/deep_learning/mlp
.. _cnndoc: docs/source/content/deep_learning/cnn.rst
.. _cnncode: code/deep_learning/cnn
.. _aedoc: docs/source/content/deep_learning/autoencoder.rst
.. _aecode: code/deep_learning/autoencoder
.. _rnndoc: code/deep_learning/rnn/rnn.ipynb
.. _rnncode: code/deep_learning/rnn/rnn.py
+--------------------------------------------------------------------+-------------------------------+---------------------------+
| Title | Code | Document |
+====================================================================+===============================+===========================+
| Neural Networks Overview | `Python `_ | `Tutorial `_ |
+--------------------------------------------------------------------+-------------------------------+---------------------------+
| Convolutional Neural Networks | `Python `_ | `Tutorial `_ |
+--------------------------------------------------------------------+-------------------------------+---------------------------+
| Autoencoders | `Python `_ | `Tutorial `_ |
+--------------------------------------------------------------------+-------------------------------+---------------------------+
| Recurrent Neural Networks | `Python `_ | `IPython `_ |
+--------------------------------------------------------------------+-------------------------------+---------------------------+
========================
Pull Request Process
========================
Please consider the following criterions in order to help us in a better way:
1. The pull request is mainly expected to be a link suggestion.
2. Please make sure your suggested resources are not obsolete or broken.
3. Ensure any install or build dependencies are removed before the end of the layer when doing a
build and creating a pull request.
4. Add comments with details of changes to the interface, this includes new environment
variables, exposed ports, useful file locations and container parameters.
5. You may merge the Pull Request in once you have the sign-off of at least one other developer, or if you
do not have permission to do that, you may request the owner to merge it for you if you believe all checks are passed.
========================
Final Note
========================
We are looking forward to your kind feedback. Please help us to improve this open source project and make our work better.
For contribution, please create a pull request and we will investigate it promptly. Once again, we appreciate
your kind feedback and support.
========================
Developers
========================
**Supervisor and creator of the project**: Amirsina Torfi [`GitHub
`_, `Personal Website
`_, `Linkedin
`_ ]
**Developers**: Amirsina Torfi, Brendan Sherman\*, James E Hopkins\* [`Linkedin `_], Zac Smith [`Linkedin `_]
**NOTE**: This project has been developed as a capstone project offered by [`CS 4624 Multimedia/ Hypertext course at Virginia Tech `_] and
Supervised and supported by [`Machine Learning Mindset `_].
\*: equally contributed
======================
Citation
======================
If you found this course useful, please kindly consider citing it as below:
.. code:: shell
@software{amirsina_torfi_2019_3585763,
author = {Amirsina Torfi and
Brendan Sherman and
Jay Hopkins and
Eric Wynn and
hokie45 and
Frederik De Bleser and
李明岳 and
Samuel Husso and
Alain},
title = {{machinelearningmindset/machine-learning-course:
Machine Learning with Python}},
month = dec,
year = 2019,
publisher = {Zenodo},
version = {1.0},
doi = {10.5281/zenodo.3585763},
url = {https://doi.org/10.5281/zenodo.3585763}
}